Abstract: Aiming at the problem of the least square estimation error in traditional distance vector-hop (DV-Hop) algorithm for wireless sensor networks,a fusion algorithm of improved grey wolf optimization(GWO) and DV-Hop was proposed.Firstly,the traditional DV-Hop algorithm is used to estimate the distance between beacon nodes and unknown nodes.Secondly,GWO algorithm with adaptive strategy is employed to replace the least square method to estimate the position of unknown nodes.The improvements include the introduction of good-points sets for initial wolves individuals to improve the ergodicity of the initial population.In order to speed up the update of population position,the control parameter a is adaptive adjusted and the population position is updated according to the fitness values of α,β and σ.Finally,the mirroring strategy is adopted to deal with the estimated cross-border node.Experimental results show that the proposed algorithm has high positioning accuracy and good stability compared with the traditional DV-Hop algorithm,the literature [1]’s algorithm and the literature [2]’s algorithm.

Key words: Adaptive adjusted strategy, DV-Hop algorithm, Good-points sets, Grey wolf optimization algorithm, Wireless sensor network